Adaptive object tracking algorithm for autonomous machine applications

ABSTRACT

In various examples, lane location criteria and object class criteria may be used to determine a set of objects in an environment to track. For example, lane information, freespace information, and/or object detection information may be used to filter out or discard non-essential objects (e.g., objects that are not in an ego-lane or adjacent lanes) from objects detected using an object detection algorithm. Further, objects corresponding to non-essential object classes may be filtered out to generate a final filtered set of objects to be tracked that may be of a lower quantity than the actual number of detected objects. As a result, object tracking may only be executed on the final filtered set of objects, thereby decreasing compute requirements and runtime of the system without sacrificing object tracking accuracy and reliability with respect to more pertinent objects.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/014,075, filed Sep. 8, 2020, which is hereby incorporated byreference in its entirety.

BACKGROUND

Autonomous driving systems and advanced driver assistance systems (ADAS)may leverage sensors, such as cameras, LIDAR sensors, RADAR sensors,etc., to perform various tasks—such as object detection, objecttracking, lane keeping, lane changing, lane assignment, cameracalibration, turning, path planning, and localization. For example, forautonomous and ADAS systems to operate independently and efficiently, anunderstanding of the surrounding environment of the vehicle in real-timeor near real-time may be generated. Essential to this understanding isobject tracking, where a location of objects over time may be used toinform the autonomous system of movement patterns of surroundingobjects, locations of surrounding objects, future estimated locations ofsurrounding objects, and the like.

As an example, the tracked object information may prove useful whenmaking path planning, obstacle avoidance, and/or control decisions. Asthe environment or scene becomes more congested, and thus more complexto process—such as in urban and/or semi-urban driving environments—anunderstanding of object locations over time may become more essential tothe safe operation of the vehicle. However, conventional systems thatemploy object tracking—such as those that implement image-based objecttracking methods—detect and track each object in the environment of thevehicle. This is true regardless of the location of the object, adirection of travel of the object, and/or an importance of the objectfor decisions regarding safe and effective driving by the vehicle. Asthe number of detected objects increases—such as in more complex drivingscenes—the compute resource requirements increase and the processingtimes increase to a point where real-time or near real-time deploymentof the system at an acceptable level of safety becomes unattainable.

SUMMARY

Embodiments of the present disclosure relate to adaptive object trackingalgorithms for autonomous machine applications. Systems and methods aredisclosed that detect objects, and then filter out detected objects toreduce the compute burden while also decreasing run-time for time-boundoperations—such as object tracking—in real-time or near real-time. Incontrast to conventional systems, such as those described above, thesystems and methods of the present disclosure may use lane locationcriteria and/or essential object class criteria to filter out a subsetof detected objects of less importance to accurate and reliable objecttracking. As a result, a more focused subset of the detected objects maybe processed for object tracking, which in turn reduces the computeburden and the runtime of the system.

For example, lane information, free-space information, and/or objectsemantic or location information may be used to assign detected objectsto lanes, and to filter out non-essential objects prior to processing byan object tracker. A lane graph may be generated using lane informationand/or free space information to represent locations and poses ofdetected lanes to aid in localizing the ego-vehicle and other detectedobjects in the environment. Locations of the detected lanes may bemapped to locations of the detected objects within the correspondinglanes, and the non-essential objects may be filtered out from thedetected objects based on this mapping and lane location criteria. Forexample, the lane location criteria may indicate essential lanes (e.g.,ego-lane, right adjacent lane to the ego-lane, left adjacent lane to theego-lane, etc.) where essential objects are to be tracked, and objectsin non-essential lanes may be filtered out to generate a filtered set ofobjects. The filtered set of objects associated with non-essentialobject classes (e.g., pedestrians, traffic lights, stop signs, animals,certain vehicle classes, etc.) may be also be filtered out. As such, theefficiency of the system may be increased without sacrificing quality orreliability as only objects associated with essential object classes(e.g., cars, trucks, motorcyclists, bicyclists) and those located inessential lanes may be provided to an object tracker for tracking.

As a result of reducing the number of objects that need to be trackedover time, the process of detecting and tracking objects may becomparatively less time consuming, less computationally intense, andmore scalable as the amount of data to be processed by an object trackeris reduced. In addition, by reducing the amount of data transmitted perframe, the number of frames processed per second may be increased togenerate additional results in the same amount of time—therebyincreasing the granularity of the object tracking computations of thesystem.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for adaptive object tracking algorithmfor autonomous machine applications are described in detail below withreference to the attached drawing figures, wherein:

FIG. 1A is an example data flow diagram illustrating an example processfor object tracking, in accordance with some embodiments of the presentdisclosure;

FIG. 1B is an example data flow diagram illustrating an example processfor filtering objects to be tracked, in accordance with some embodimentsof the present disclosure;

FIG. 2 is a block diagram of an example object tracking system, inaccordance with some embodiments of the present disclosure;

FIG. 3A is an illustration of an example mapping between detectedobjects and detected lanes, in accordance with some embodiments of thepresent disclosure;

FIG. 3B is an illustration of an example filtered set of objectsgenerated by an object filter based on lane information, in accordancewith some embodiments of the present disclosure;

FIG. 3C is an illustration of an example remaining list of objectsgenerated by the object filer based on object class, in accordance withembodiments of the present disclosure;

FIG. 4 is a flow diagram showing a method for detecting objects, andthen filtering out detected objects to reduce the compute burden whilealso decreasing run-time for time-bound operations, in accordance withsome embodiments of the present disclosure;

FIG. 5 is a flow diagram showing a method 500 for detecting objects, andthen filtering out detected objects to reduce the compute burden whilealso decreasing run-time for time-bound operations, in accordance withsome embodiments of the present disclosure;

FIG. 6A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 6B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 6A, in accordance with someembodiments of the present disclosure;

FIG. 6C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 6A, in accordance with someembodiments of the present disclosure;

FIG. 6D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 6A, in accordancewith some embodiments of the present disclosure;

FIG. 7 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure; and

FIG. 8 is a block diagram of an example data center suitable for use inimplementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to adaptive object trackingalgorithm for autonomous machine applications. Although the presentdisclosure may be described with respect to an example autonomousvehicle 600 (alternatively referred to herein as “vehicle 600” or“ego-vehicle 600,” an example of which is described herein with respectto FIGS. 6A-6D), this is not intended to be limiting. For example, thesystems and methods described herein may be used by non-autonomousvehicles, semi-autonomous vehicles (e.g., in one or more advanced driverassistance systems (ADAS)), robots, warehouse or factory vehicles ormachines, off-road vehicles, flying vessels, boats, and/or other vehicletypes. In addition, although the present disclosure may be describedwith respect to autonomous driving, this is not intended to be limiting.For example, the systems and methods described herein may be used inrobotics (e.g., object detection and tracking for robotics), aerialsystems (e.g., object detection and tracking for a drone or other aerialvehicle), boating systems (e.g., object detection and tracking forwatercraft), simulation environments (e.g., for simulating objectdetection and tracking of virtual vehicles within a virtual simulationenvironment), and/or other technology areas, such as for objectdetection and object tracking.

In contrast to the conventional techniques, the current systems andmethods provide techniques to detect objects, and then filter outdetected objects to reduce the compute burden while also decreasingrun-time for time-bound operations—such as object tracking. For example,outputs from various sensors (e.g., cameras, RADAR sensors, LIDARsensors, etc.) of a vehicle may be processed in real-time or nearreal-time—e.g., using one or more computer vision algorithms, machinelearning models, and/or deep neural networks (DNNs)— to discardnon-essential objects prior to executing a tracking algorithm fortracking the objects in the surrounding environment. For example, liveperception of the vehicle may be used to detect and classify objectsonly in the vehicle's current lane, a lane to the immediate right of thevehicle's current lane, and/or a lane to the immediate left of thevehicle's current lane. In this way, outputs from one or morealgorithms—such as a lane detection algorithm, an object detection andclassification algorithm, and/or a free-space detection algorithm—may beused to classify and/or determine objects of importance in a vehicle'spath prior to providing the object tracking algorithm with informationregarding the to-be-tracked objects. As such, by using lane information,free-space information, object class information, object in pathanalysis (OIPA), and/or vehicle localization information, the process ofdetecting and classifying essential objects in a vehicle's environmentmay be comparatively less time-consuming, less computationally intense,and more scalable as the system may learn to determine, store, and trackinformation regarding only essential objects in a vehicle's environment.

The present systems and methods enable real-time or near real-timetracking of objects in an environment of an ego-vehicle. For example,using various processing steps to filter out certain detected objectsprior to tracking, only the most important or highest priority objectinformation is passed to an object tracking algorithm for trackingobjects in the environment. In order to filter out the objects, variouscomputer vision algorithms, machine learning models, and/or deep neuralnetworks (DNNs) may be employed to process sensor data generated bysensors of the ego-vehicle to generate an understanding—e.g., via a lanegraph, localization thereto, etc. —of the surrounding environment. Thesensor data may include sensor data generated by any type of sensor ofthe ego-vehicle (e.g., cameras, RADAR sensors, LiDAR sensors, ultrasonicsensors, global navigation satellite systems (GNSS) sensors, inertialmeasurement unit (IMU) sensors, etc.) and may be represented in anyacceptable format for processing (e.g., as images, videos, point clouds,depth maps, range images, other sensor data representations, etc.).

The sensor data may be processed for lane detection, free-spacedetection, object detection and/or classification, and, ultimately,object tracking. For example, locations, directions, orientations,classifications, and/or other information corresponding to lanes in theenvironment of the vehicle may be computed. In addition, in someembodiments, a location of a boundary that separates drivable free-spacefrom non-drivable space may be computed, and/or the regions of theenvironment that correspond to drivable free-space may be computed.Objects in the environment may also be detected and/or classified. Forexample, the objects may be detected and classified by class and/orinstance, such that unique object instances can ultimately be tracked byan object tracker. In addition to this information, GNSS sensors, IMUs,high definition (HD) maps, and/or other localization information may beused to localize the ego-vehicle and/or other objects relative to thecomputed or predicted environment information (e.g., a lane graphs). Forexample, a lane graph may be generated using the computed laneinformation, and the localization information of the ego-vehicle. Assuch, the lane graph may be used to determine the layout of the drivingsurface, and ultimately, the locations of objects thereon. In someembodiments, information corresponding the drivable free-space may alsobe included in the lane graph, and/or may be used to map which objectsto populate to the lanes (e.g., objects on sidewalks may not be tracked,while objects on the driving surface may).

The mapping between the lanes and the objects may then be used to filterout the non-essential objects such that a set of essential objects maybe determined. For example, object-in-path analysis (OIPA) may beexecuted using the mapping to filter out at least a subset of thedetected actors. In a non-limiting example, each of the objects not inthe ego-lane, a right adjacent lane of the ego-lane, and a left adjacentlane of the ego-lane—as determined using the OIPA, the lane graph, andthe mapping—may be filtered out. The filtered set of objects may then bepassed to an object class associator that may use the object detectioninformation (e.g., class and/or instance information) corresponding tothe filtered set of objects to further filter out objects correspondingto non-essential classes. As such, in a non-limiting example, each ofthe remaining objects that is not a car, truck, bicyclist, ormotorcyclist may be filtered out. The remaining list of objects may thenbe passed to an object tracker, and the object tracker may begin orcontinue (e.g., from any number of prior instances of the trackingalgorithm) to track the objects.

In some embodiments, processing may be split between one or moregraphics processing units (GPUs) and one or more central processingunits (CPUs). For example, the computing or predicting of the laneinformation, free-space information, object detections and/orclassifications, and/or object tracking may be executed using a GPU(s),while the processing of these outputs may be executed using a CPU(s). Inaddition, in some embodiments, two or more system on chips (SoCs) may beused to perform the operations. As a result, this tracking data may bepassed between the various SoCs using transmission control protocol(TCP), and the size of the data passed between SoCs has a direct impacton the latency of the system. As such, by reducing the number of objectsthat need to be tracked over time, the amount of data transmittedbetween the SoCs may be reduced, thereby increasing the efficiency ofthe system. In addition, by reducing the amount of data transmitted perframe, the number of frames processed per second may be increased togenerate additional results in the same amount of time—therebyincreasing the accuracy and granularity of the object trackingcomputations of the system.

Now referring to FIG. 1A, FIG. 1A is an example data flow diagramillustrating an example process 100 for detecting objects, and thenfiltering out detected objects to reduce the compute burden while alsodecreasing run-time for time-bound operations—such as object tracking,in accordance with some embodiments of the present disclosure. It shouldbe understood that this and other arrangements described herein are setforth only as examples. At a high level, the process 100 may include oneor more detectors 104 receiving one or more inputs, such as sensor data102, and generating one or more outputs, such as one or more lanedetections, object detections, and/or free-space detections, which canbe used to generate lane graphs, and/or object lane assignments using alane graph generator 106 and/or object lane assignor 108. Object filter110 may use the outputs of the lane assignor 108 to filter detectedobjects. Although the sensor data 102 is primarily discussed withrespect to image data representative of images, this is not intended tobe limiting, and the sensor data 102 may include other types of sensordata such as, but not limited to, LIDAR data, SONAR data, RADAR data,and/or the like—e.g., as generated by one or more sensors of the vehicle600 (FIGS. 6A-6D).

The process 100 may include generating and/or receiving sensor data 102from one or more sensors. The sensor data 102 may be received, as anon-limiting example, from one or more sensors of a vehicle (e.g.,vehicle 600 of FIGS. 6A-6C and described herein). The sensor data 102may be used by the vehicle 600, and within the process 100, to navigateits environment in real-time or near real-time. The sensor data 102 mayinclude, without limitation, sensor data 102 from any of the sensors ofthe vehicle including, for example and with reference to FIGS. 6A-6C,global navigation satellite systems (GNSS) sensor(s) 658 (e.g., GlobalPositioning System sensor(s)), RADAR sensor(s) 660, ultrasonic sensor(s)662, LIDAR sensor(s) 664, inertial measurement unit (IMU) sensor(s) 666(e.g., accelerometer(s), gyroscope(s), magnetic compass(es),magnetometer(s), etc.), microphone(s) 676, stereo camera(s) 668,wide-view camera(s) 670 (e.g., fisheye cameras), infrared camera(s) 672,surround camera(s) 674 (e.g., 360 degree cameras), long-range and/ormid-range camera(s) 678, speed sensor(s) 644 (e.g., for measuring thespeed of the vehicle 600), and/or other sensor types. As anotherexample, the sensor data 102 may include virtual sensor data generatedfrom any number of sensors of a virtual vehicle or other virtual objectin a virtual (e.g., testing) environment. In such an example, thevirtual sensors may correspond to a virtual vehicle or other virtualobject in a simulated environment (e.g., used for testing, training,and/or validating neural network performance), and the virtual sensordata may represent sensor data captured by the virtual sensors withinthe simulated or virtual environment.

In some embodiments, the sensor data 102 may include image datarepresenting an image(s), image data representing a video (e.g.,snapshots of video), and/or sensor data representing representations ofsensory fields of sensors (e.g., depth maps for LIDAR sensors, a valuegraph for ultrasonic sensors, etc.). Where the sensor data 102 includesimage data, any type of image data format may be used, such as, forexample and without limitation, compressed images such as in JointPhotographic Experts Group (JPEG) or Luminance/Chrominance (YUV)formats, compressed images as frames stemming from a compressed videoformat such as H.264/Advanced Video Coding (AVC) or H.265/HighEfficiency Video Coding (HEVC), raw images such as originating from RedClear Blue (RCCB), Red Clear (RCCC), or other type of imaging sensor,and/or other formats. In addition, in some examples, the sensor data 102may be used within the process 100 without any pre-processing (e.g., ina raw or captured format), while in other examples, the sensor data 102may undergo pre-processing (e.g., noise balancing, demosaicing, scaling,cropping, augmentation, white balancing, tone curve adjustment, etc.,such as using a sensor data pre-processor (not shown)). The sensor data102 may include original images (e.g., as captured by one or more imagesensors), down-sampled images, up-sampled images, cropped or region ofinterest (ROI) images, otherwise augmented images, and/or a combinationthereof. As used herein, the sensor data 102 may reference unprocessedsensor data, pre-processed sensor data, or a combination thereof.

The detectors 104 may include a lane detector 104A, a freespace detector104B, and/or an object detector 104C. The detectors 104 may include oneor more computer vision algorithms, machine learning models, and/or DNNstrained to compute lane information (e.g., location, pose, geometry,etc.), freespace boundary locations, and/or object information (e.g.,location, pose, class, etc.) using sensor data 102. For example, andwithout limitation, the detectors 104 may include machine learningmodels using linear regression, logistic regression, decision trees,support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), Kmeans clustering, random forest, dimensionality reduction algorithms,gradient boosting algorithms, neural networks (e.g., auto-encoders,convolutional, recurrent, perceptrons, long/short term memory/LSTM,Hopfield, Boltzmann, deep belief, deconvolutional, generativeadversarial, liquid state machine, etc.), areas of interest detectionalgorithms, computer vision algorithms, and/or other types of machinelearning models or algorithms.

As an example, such as where the detectors 104 include a DNN— or morespecifically a convolutional neural network (CNN), the detectors 104 mayinclude any number of layers. One or more of the layers may include aninput layer. The input layer may hold values associated with the sensordata 102 (e.g., before or after post-processing). For example, when thesensor data 102 is an image, the input layer may hold valuesrepresentative of the raw pixel values of the image(s) as a volume(e.g., a width, a height, and color channels (e.g., RGB), such as32×32×3).

One or more layers may include convolutional layers. The convolutionallayers may compute the output of neurons that are connected to localregions in an input layer, each neuron computing a dot product betweentheir weights and a small region they are connected to in the inputvolume. A result of the convolutional layers may be another volume, withone of the dimensions based on the number of filters applied (e.g., thewidth, the height, and the number of filters, such as 32×32×12, if 12were the number of filters).

One or more of the layers may include a rectified linear unit (ReLU)layer. The ReLU layer(s) may apply an elementwise activation function,such as the max (0, x), thresholding at zero, for example. The resultingvolume of a ReLU layer may be the same as the volume of the input of theReLU layer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16×16×12 from the 32×32×12input volume).

One or more of the layers may include one or more fully connectedlayer(s). Each neuron in the fully connected layer(s) may be connectedto each of the neurons in the previous volume. The fully connected layermay compute class scores, and the resulting volume may be 1×1× number ofclasses. In some examples, the CNN may include a fully connectedlayer(s) such that the output of one or more of the layers of the CNNmay be provided as input to a fully connected layer(s) of the CNN. Insome examples, one or more convolutional streams may be implemented bythe detectors 104, and some or all of the convolutional streams mayinclude a respective fully connected layer(s).

In some non-limiting embodiments, the detectors 104 may include a seriesof convolutional and max pooling layers to facilitate image featureextraction, followed by multi-scale dilated convolutional andup-sampling layers to facilitate global context feature extraction.

Although input layers, convolutional layers, pooling layers, ReLUlayers, and fully connected layers are discussed herein with respect tothe detectors 104, this is not intended to be limiting. For example,additional or alternative layers may be used in the machine learningmodel(s) 104, such as normalization layers, SoftMax layers, and/or otherlayer types.

In embodiments where the detectors 104 includes a CNN, different ordersand numbers of the layers of the CNN may be used depending on theembodiment. In other words, the order and number of layers of thedetectors 104 is not limited to any one architecture.

In addition, some of the layers may include parameters (e.g., weightsand/or biases), such as the convolutional layers and the fully connectedlayers, while others may not, such as the ReLU layers and poolinglayers. In some examples, the parameters may be learned by the detectors104 during training. Further, some of the layers may include additionalhyper-parameters (e.g., learning rate, stride, epochs, etc.), such asthe convolutional layers, the fully connected layers, and the poolinglayers, while other layers may not, such as the ReLU layers. Theparameters and hyper-parameters are not to be limited and may differdepending on the embodiment.

The detectors 104 may use the sensor data 102 to detect lanes, freespaceboundaries, and/or objects, which may ultimately be applied to the lanegraph generator 106 to generate a lane graph and/or the object laneassignor 108 to associate objects with lanes in the lane graph. Forexample, the lane detector 104A may be trained or programmed to detectlanes and/or associate lane classifications in the sensor data 102. Forexample, the lane detector 104A may compute locations, directions,orientations, classifications, and/or other information corresponding tolanes in the environment of the vehicle as determined using the sensordata 102.

The freespace detector 104B may be trained or programmed to detectdrivable free-space and/or non-drivable space—or boundariescorresponding to the division thereof—in the vehicle's environment usingthe sensor data 102. For example, the freespace detector 104B maycompute a location of a boundary that separates drivable free-space fromnon-drivable space, and/or the regions of the vehicle's environment thatcorrespond to drivable free-space.

The lane graph generator 106 may use—as input—the outputs of the lanedetector 104A and/or the freespace detector 104B. For example, the lanegraph generator 106 may compute one or more lane graphs based on thelane and freespace detections. The object lane assignor 108 may uselocalization information from one or more of GNSS sensors, IMUS, highdefinition (HD) maps, and/or other localization information to localizethe vehicle 600 (and/or surrounding objects) relative to the computed orpredicted lane graph or environmental information. For example, the lanegraph generator 106 may generate a lane graph using the computed laneinformation from the lane detector 104A and the current locationinformation of the vehicle 600 (e.g., the lane graph may indicate anego-lane of the vehicle 600 and other lanes relative to the ego-vehiclelocation using the current location information of the vehicle 600). Assuch, the lane graph may represent the layout of the driving surface. Insome examples, the lane graph may represent the driving surface using anarray of points (e.g., pixels) to represent lanes, and include thesource of lane information (e.g., lane detector 104A, HD map),classifications of the lanes (e.g., current lane, left lane, right lane,opposing lane), and localization of the vehicle 600. In this way, thelane graph generator 106 may generate a lane graph representing lanesand localization of the vehicle 600 on the driving surface. In someexample, the lane graph generator 106 may include representations ofdrivable free-space in the lane graph.

The object detector 104C may be trained or programmed to detect and/orclassify objects in the environment of vehicle 600 based on sensor data102. For example, the objects may be detected and classified by classand/or instance, such that unique object instances can ultimately betracked by the object tracker 112. The object detector 104 may computebounding boxes around detected objects, and each detected object may beassociated with a class—for example and without limitation: car,bicycle, stop sign, yield sign, traffic light, pedestrian, truck,motorcycle, and/or other classifications of objects that the objectdetector 104C may be trained or programmed to detect. In some examples,the object detector 104C may detect locations of the objects using thesensor data. For example, the output of the object detector 104 maycorrespond to pixels at the location of an object as being associatedwith the object and/or the objects class, bounding shape information maybe output by the object detector 104C, and/or other object detectionrepresentations may be output.

The object lane assignor 108 may take as input the outputs of the lanegraph generator 106, the object detector 104C, and/or the freespacedetector 104B to generate a mapping between the objects and the lanesthat assigns or associates detected objects with lanes of the lanegraph. For example, a mapping of the objects to the lanes may begenerated using the computed lane information, the localizationinformation of the vehicle 600, and/or information about the detectedobjects (e.g., object class, object pose, object location, etc.). Forexample, a detected object may be assigned and/or mapped to a detectedlane based on pixels belonging to the object also belonging to the lane.A mapping may include locations of lanes and locations of the detectedobjects within the corresponding lanes. As such, the mapping mayrepresent not only the layout of the driving surface, but also thelocations of objects thereon. In some embodiments, informationcorresponding the drivable free-space may also be included in themapping, and/or may be used to determine which objects to include in themapping (e.g., objects on sidewalks may not be mapped, while objects onthe driving surface may). In this way, the object lane assignor 108 maymap or assign each detected object to a detected lane or a non-drivablesurface by localizing the objects to the corresponding lanes.

The object filter 110 may take as input the mapping output from theobject lane assignor 108 and/or the object detector 104C to filter outnon-essential objects from the mapped objects to determine a set ofessential objects based on various criteria—such as lane locationcriteria, object type criteria, etc. Lane location criteria may includea set of rules for filtering objects. For example, in some examples, theobject filter 110 may execute an object-in-path analysis (OIPA) based onthe mapping to filter out at least a subset of the detected objects. Forexample, each of the objects not in the vehicle's current lane, a rightadjacent lane of the current lane, and a left adjacent lane of thecurrent lane—as determined using the OIPA and the mapping—may befiltered out. Lane classes in the mapping may be used to determine theobjects in the current lane, left adjacent lane of the current lane, andright adjacent lane of the current lane. The filtered set of objects maythen be provided to one or more additional filters, or to the objecttracker 112 for tracking.

In some examples, the object filter 110 may filter the set of objects byfiltering out objects corresponding to non-essential object classes. Forexample, output of the object detector 104C— such as object locations,object classes, object instance information, etc. —may be used to filterout the objects belonging to non-essential (or undesired) objectclasses. For example, outputs of the object detector 104C correspondingto the filtered set of objects may be used by the object filter 110 tofurther filter out objects corresponding to non-essential objectclasses. In some examples, each of the objects of the filtered set ofobjects that do not belong to an object class associated with a car,truck, bicyclist, or motorcyclist may be filtered out. As such,non-essential objects (e.g., traffic lights, pedestrians) may befiltered out prior to tracking. The remaining list of objects may thenbe passed to the object tracker 112, and the object tracker 112 maybegin or continue (e.g., from any number of prior instances of thetracking algorithm) to track the remaining list of objects. It should benoted that although the filtering is described as including filteringbased on lane information followed by filtering based on object classes,this is not meant to be limiting. In some examples, the detected objectsmay be filtered based on object classes prior to filtering based on laneinformation, and/or one or more other filtering types may be executed inany order, depending on the embodiment.

The object tracker 112 may receive object and lane informationcorresponding to the remaining list of objects. The object tracker 112may have a prior list of objects currently being tracked, and the objecttracker 112 may compare the remaining list of objects with the priorlist of objects currently being tracked to continue tracking an objector start tracking a new object. As such, objects may be added and/orremoved from the prior list of objects over time. In some examples, theobject tracker 112 may receive an object association score for each ofthe remaining list of objects from the object lane assignor 108. Thescore may be based on a confidence of the associated object being in theassigned lane. Objects of the remaining list of objects scored below apredetermined score may be removed from the updated list of objects tobe tracked. The updated list of objects to be tracked may be sent tocontrol components of the vehicle 600 for further processing to assistin one or more operations of the vehicle 600—such as path planning,control, actuation, obstacle avoidance, etc.

Now referring to FIG. 1B, FIG. 1B is an example data flow diagramillustrating an example process 120 for filtering objects to be tracked,in accordance with some embodiments of the present disclosure. In someexamples, processing may be split between one or more graphicsprocessing units (GPUs) 140 and one or more central processing units(CPUs) 150. For example, the computing or predicting of the laneinformation, free-space information, object detections and/orclassifications, and/or object tracking may be executed using a GPU(s),while the processing of these outputs may be executed using a CPU(s). Assuch, a GPU(s) 140 may execute the detectors 104 and/or the objecttracker 112. The lane information, the freespace information, and theobject detection information generated by the detectors 104 mayultimately be used by the object tracker 112 to track objects in theenvironment of the vehicle 600. In this way, lane information may beused to determine essential objects prior to the object tracker 112tracking the objects. As a result, the GPU(s) 140 may be used toefficiently compute perception information, the CPU(s) 150 may processthe perception information, and the GPU(s) 140 may execute the objecttracker using feedback from the CPU(s) 150. In some embodiments, one ormore of the processing tasks may be executed in parallel using one ormore parallel processing units—e.g., parallel processing units of theGPU(s) 140.

The output of each of the detectors 104 (e.g., the lane detector 104B,the freespace detector 104B, and/or the object detector 104C) processedby the GPU(s) 140 may be provided to the CPU(s) 150 for furtherprocessing. The CPU 150 may process the outputs of the detectors 104 todetermine essential objects to be tracked for safe and efficientnavigating of vehicle 600 in real time or near real-time. The CPU 150may include a lane processor 122, a localizer 124, the lane graphgenerator 106, an object processor 126, an object in-path analyzer 128,and/or a tracking processor 112. The lane processor 122 may receive asinput lane information from the lane detector 104A. The lane processor122 may process the detected lane location information and/or laneclasses.

The localizer 124 may take as input the output of the lane processor 122to localize the vehicle 600 within the processed lanes. The localizer124 may also localize the vehicle based on localization information fromGNSS sensors, IMUs, high definition (HD) maps, and/or other localizationinformation. The lane graph generator 106 may use the processed laneinformation and the localization information to generate the lane graphassociated with the sensor data 102. For example, the lane graphgenerator 106 may generate a lane graph using the computed laneinformation from the lane processor 122 and the localization informationfrom the localizer 124. As such, the lane graph may represent the layoutof the driving surface. In some examples, the lane graph may representthe driving surface using an array of points (e.g., pixels) to representlanes, and include the source of lane information (e.g., lane detector104A, HD map), classifications of the lanes (e.g., current lane, leftlane, right lane, opposing lane, cross lanes), and/or localization ofthe vehicle 600.

The object processor 126 may use outputs of the object detector 104C toprocess detected objects. The object processor 126 may process theobject detections to determine object locations and/or classificationsfor each detected object. As a non-limiting example, pixels at objectlocations may be associated with the corresponding detected objectsand/or classifications. The object in-path analyzer 128 may determinelane location and lane class information for the ego-lane (e.g., currentlane of the vehicle 600), right adjacent lane to the ego-lane, and theleft adjacent lane to the ego-lane. The lane location and lane classinformation generated by the object in-path analyzer 128 may include anarray of points (e.g., pixels) to represent each of the ego-lane, rightadjacent lane to the ego-lane, and the left adjacent lane to theego-lane. The object in-path analyzer 128 may associate detected objectswith corresponding lanes based on locations of the detected objectsbeing within locations of the detected lanes in the lane graph. Eachobject may be associated with a lane or a non-drivable region within thelane graph.

The object lane assignor 108 may take the outputs of the object in-pathanalyzer 128 as input to perform object filtering operations based onlane location criteria and/or object class criteria. The object laneassignor 108 may generate a mapping associating locations of lanes andlocations of objects within the lanes. The object lane assignor 108 mayfilter the detected objects to determine a set of essential objectsbased on a lane location criteria. The lane location criteria mayinclude rules regarding filtering out detected objects that are notassociated with a subset of lanes (e.g., ego-lane, right adjacent laneto the ego-lane, left adjacent lane to the ego lane). Further, in someexamples, the object lane assignor 108 may take in as input the classesassociated with each object of the filtered set of objects as processedby the object processor 126. The object lane assignor 108 may furtherfilter out objects from the filtered set of objects based on the objectclass criteria indicating the object classes as high importance/priorityobject classes (e.g., cars, trucks, motorcyclists, bicyclists) and/ornon-priority object classes (e.g., pedestrians, traffic signals). Theobjects of the filtered set of objects may then be filtered to removeobjects that are associated with non-priority object classes.

The object lane assignor 108 may also generate association scores foreach pair of object and lane in the remaining set of essential orprioritized objects. The association score may indicate a probabilitythat the object is located within a particular essential lane (e.g.,ego-lane, right adjacent lane to ego-lane, left adjacent lane toego-lane), and/or a probability that the object belongs an essential orprioritized object class. A predetermined threshold score may be used todetermine whether an essential or prioritized object should be predictedand/or updated in the object tracker 112. At option 134, when anassociation score associated with an essential or prioritized object isbelow the threshold, the object is removed from consideration at block132. If the association score is above the threshold, the associatedessential or prioritized object may be predicted at block 136A at thecorresponding location within the corresponding lane. Further, if theessential or prioritized object is present in the prior tracked objectslist, the location of the essential or prioritized object may be updatedin the current tracked object lists at block 136B. If an object presentin the prior tracked objects list is not detected in the currentinstance of sensor data 102, the object may be removed from the currenttracked objects list.

The remaining list of objects may then by processed by the trackingprocessor 130 to generate and/or update the tracked objects list withlocations and/or classes of the remaining list of objects. The objecttracker 112 may then be provided with the current tracked objects listsfor tracking.

Now referring to FIG. 2 , FIG. 2 illustrates an example system 200 forpossessing the operations, in accordance with some embodiments of thepresent disclosure. Two SoCs may be used to process the operationsdescribed herein—e.g., with respect to FIGS. 1A-1B. The operations maybe divided between the first SoC 210 and the second SoC 240. Forexample, the first SoC 210 may include an object detector 212, a lanegraph generator 214, an object lane assignor 216, an object filter 218,an object tracker 220 and/or a control component(s) 222. The second SoC240 may include an object detector(s) 242—e.g., one or more cameraobstacle perception modules. For example, the second SoC 240 may includean N number of camera obstacle perception modules, and the remainingmodules or processes may execute on the first SoC 210. In such anexample, the network 260 may be used to pass data between the componentsof the first SoC 210 and the second SoC 240. In some examples, thenetwork 260 may utilize a transmission control protocol (TCP) totransmit data between the two SoCs. For example, the output of theobject detector 242 of the second SoC 240 may be transmitted to and/orused by the lane graph generator 214, the object lane assigner 216, theobject filter 218, the object tracker 220, and/or other components ofthe first SoC 210 via the network 260 for processing. The illustratedSoC configurations are provided for example purposes only, andadditional and/or alternative SoC configurations may be used withoutdeparting from the scope of the present disclosure. For example, wheretwo or more SoCs are used, modules and/or associated tasks may bedivided among the SoCs to improve efficiency and/or to provideadditional resources to the process. In one or more embodiments, usingtwo or more SoCs may allow for more parallel processing—not only withrespect to individual discrete hardware components but also across twoor more hardware components.

Referring now to FIGS. 3A-3C, FIGS. 3A-3C illustrate example objectfiltering applied to a mapping between detected objects and detectedlanes, in accordance with some embodiments of the present disclosure.For example, FIG. 3A illustrates an example mapping between detectedobjects and detected lanes output from an object lane assignor (e.g.,the object lane assignor 108) for an image 300. Objects in an image maybe detected using bounding boxes (e.g., cars 312A-312R, 314, 316A-316B,318A-318C; pedestrian 320; traffic light 322). Each object may be mappedand/or assigned to a lane (e.g., lanes 342A-342C) and/or non-drivableregion (e.g., non-drivable regions 344A-344C). For example, car 314 maybe mapped to lane 342C, cars 316A and 316B may be mapped to lane 342A,pedestrian 320 may be mapped to the non-drivable region 344A, and thetraffic light 322 may be mapped to the non-drivable region 344B.

Now referring to FIG. 3B, FIG. 3B illustrates an example filtered set ofobjects generated by an object filter (e.g., the object filter 110)based on lane information, in accordance with some embodiments of thepresent disclosure. Objects mapped to lanes other than the vehicle'scurrent lane (e.g., 342B), right adjacent lane to the current lane(e.g., lane 342A), left adjacent lane to the current lane (e.g., lane342C) may be filtered out of the detected objects to generate thefiltered set of objects. The filtered set of objects may then includecars 316A and 316B mapped to right adjacent lane 342A, car 314 mapped toleft adjacent lane 342C, pedestrian 320, and traffic light 322. Forexample, cars 312A-312R, and cars 318A-318C may be filtered out ordiscarded based on being mapped to lanes other than the current lane,the right adjacent lane to the current lane, and the left adjacent laneto the current lane (e.g., lanes on the opposite side on the divider,lanes for cross traffic). In this way, the object filter 110 may removenon-essential or non-priority objects present in non-essential lanes togenerate a filtered set of objects.

Referring to FIG. 3C, FIG. 3C illustrates an example remaining list ofobjects generated by the object filter (e.g., the object filter 110)based on object class, in accordance with some embodiments of thepresent disclosure. Objects in the filtered set of objects (e.g., car316A, car 316B, car 314, pedestrian 320, traffic light 322) may befurther filtered to remove objects corresponding to non-essential objectclasses. The output of the object detector 104C (e.g., object locations,classification) may be used to determine objects in the filtered set ofobjects that correspond to non-essential object classes. As such,pedestrian 320 and traffic light 322 may be removed to generate theremaining list of objects (e.g., car 316A, car 316B, car 314) asevidenced by the remaining bounding boxes as essential objectscorresponding to essential object classes. In this way, the objectfilter 110 may further remove objects corresponding to non-essentialobject classes prior to sending the remaining list of objects to theobject tracker 112 for tracking in order to ultimately process and/orconduct safe driving operations in real-time or near real-time.

Now referring to FIGS. 4-5 , each block of methods 400 and 500,described herein, comprises a computing process that may be performedusing any combination of hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. The methods may also be embodied ascomputer-usable instructions stored on computer storage media. Themethods may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, methods 400 and500 are described, by way of example, with respect to the processes 100and 120 of FIGS. 1A and 1B, respectively. However, these methods mayadditionally or alternatively be executed in any one process by any onesystem, or any combination of processes and systems, including, but notlimited to, those described herein.

FIG. 4 is a flow diagram showing a method 400 for detecting objects, andthen filtering out detected objects to reduce the compute costs whilealso decreasing run-time for time-bound operations, in accordance withsome embodiments of the present disclosure. The method 400, at blockB402, includes computing, using sensor data generated by one or moresensors of an ego-vehicle, lane information, object detectioninformation, and object classification information. For example, thedetectors 104 may compute, using the sensor data 102, lane informationusing the lane detector 104A, and object detection information andobject class information using the object detector 104C.

The method 400, at block B404, includes generating, based at least inpart on the lane information and the object detection information, amapping representative of first locations of lanes and second locationsof objects within the lanes. For example, the object lane assignor 108may generate a mapping representative of first locations of lanes andsecond locations of objects within the lanes.

The method 400, at block B406, includes performing, based at least inpart on the mapping, an object-in-path analysis (OIPA) to determine afirst subset of the objects within a subset of the lanes. For example,the object filter 110 may determine a first subset of objects (e.g.,filtered set of objects) by performing an OIPA analysis based on themapping.

The method 400, at block B408, includes determining, based at least inpart on the object classification information, classes associated witheach object of the first subset of the objects. For example, the objectfilter 110 may determine classes associated with each object of thefirst subset of objects based on the object classification informationoutput by the object detector 104C.

The method 400, at block B410, includes filtering out, based at least inpart on the classes, one or more objects from the first subset of theobjects to determine a second subset of the objects. For example, theobject filter 110 may filter out one or more objects (e.g., objectscorresponding to non-essential object classes) from the first subset ofobjects to determine a second subset of objects (e.g., remaining list ofobjects) based on the classes.

The method 400, at block B412, includes transmitting data representativeof the second subset of the objects to an object tracking algorithm fortracking the second subset of the objects. For example, datarepresentative of the second subset of objects may be transmitted to theobject tracker 112 for tracking the second subset of objects.

FIG. 5 is a flow diagram showing a method 500 for detecting objects, andthen filtering out detected objects to reduce compute costs while alsodecreasing run-time for time-bound operations, in accordance with someembodiments of the present disclosure. The method 500, at block B502,includes computing, using sensor data generated by the one or moresensors of the ego-vehicle, lane information and object detectioninformation. For example, the detectors 104 may compute, using thesensor data 102, lane information using the lane detector 104A, andobject detection information using the object detector 104C.

The method 500, at block B504, includes localizing objects to lanes togenerate a mapping based at least in part on the lane information andthe object detection information. For example, the object lane assignor108 may generate a mapping that localizes objects to lanes based on theoutputs of the lane detector 104A, and the object detector 104C.

The method 500, at block B506, includes determining a subset of theobjects within a subset of the lanes based at least in part on themapping and a lane location criteria. For example, the object filter 110may determine locations of a filtered set of objects and/or remaininglist of objects within the current lane of the vehicle, the rightadjacent lane to the current lane, and the left adjacent lane to thecurrent lane based on the mapping and object class information.

The method 500, at block B508, includes transmitting data representativeof the subset of the objects to an object tracking algorithm fortracking the second subset of the objects. For example, datarepresentative of the filtered set of objects and/or remaining list ofobjects may be transmitted to the object tracker 112 for tracking thefiltered set of objects and/or remaining list of objects.

Example Autonomous Vehicle

FIG. 6A is an illustration of an example autonomous vehicle 600, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 600 (alternatively referred to herein as the “vehicle600”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,and/or another type of vehicle (e.g., that is unmanned and/or thataccommodates one or more passengers). Autonomous vehicles are generallydescribed in terms of automation levels, defined by the National HighwayTraffic Safety Administration (NHTSA), a division of the US Departmentof Transportation, and the Society of Automotive Engineers (SAE)“Taxonomy and Definitions for Terms Related to Driving AutomationSystems for On-Road Motor Vehicles” (Standard No. J3016-201806,published on Jun. 15, 2018, Standard No. J3016-201609, published on Sep.30, 2016, and previous and future versions of this standard). Thevehicle 600 may be capable of functionality in accordance with one ormore of Level 3— Level 5 of the autonomous driving levels. For example,the vehicle 600 may be capable of conditional automation (Level 3), highautomation (Level 4), and/or full automation (Level 5), depending on theembodiment.

The vehicle 600 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 600 may include a propulsion system650, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 650 may be connected to a drive train of the vehicle600, which may include a transmission, to enable the propulsion of thevehicle 600. The propulsion system 650 may be controlled in response toreceiving signals from the throttle/accelerator 652.

A steering system 654, which may include a steering wheel, may be usedto steer the vehicle 600 (e.g., along a desired path or route) when thepropulsion system 650 is operating (e.g., when the vehicle is inmotion). The steering system 654 may receive signals from a steeringactuator 656. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 646 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 648 and/or brakesensors.

Controller(s) 636, which may include one or more system on chips (SoCs)604 (FIG. 6C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle600. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 648, to operate thesteering system 654 via one or more steering actuators 656, to operatethe propulsion system 650 via one or more throttle/accelerators 652. Thecontroller(s) 636 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 600. The controller(s) 636 may include a first controller 636for autonomous driving functions, a second controller 636 for functionalsafety functions, a third controller 636 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 636 forinfotainment functionality, a fifth controller 636 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 636 may handle two or more of the abovefunctionalities, two or more controllers 636 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 636 may provide the signals for controlling one ormore components and/or systems of the vehicle 600 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 658 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 660, ultrasonic sensor(s) 662, LIDARsensor(s) 664, inertial measurement unit (IMU) sensor(s) 666 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 696, stereo camera(s) 668, wide-view camera(s) 670(e.g., fisheye cameras), infrared camera(s) 672, surround camera(s) 674(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 698,speed sensor(s) 644 (e.g., for measuring the speed of the vehicle 600),vibration sensor(s) 642, steering sensor(s) 640, brake sensor(s) (e.g.,as part of the brake sensor system 646), and/or other sensor types.

One or more of the controller(s) 636 may receive inputs (e.g.,represented by input data) from an instrument cluster 632 of the vehicle600 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 634, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle600. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 622 of FIG. 6C), location data(e.g., the vehicle's 600 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 636,etc. For example, the HMI display 634 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 600 further includes a network interface 624 which may useone or more wireless antenna(s) 626 and/or modem(s) to communicate overone or more networks. For example, the network interface 624 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 626 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 6B is an example of camera locations and fields of view for theexample autonomous vehicle 600 of FIG. 6A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle600.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 600. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 600 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 636 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (“LDW”), Autonomous Cruise Control(“ACC”), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 670 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.6B, there may any number of wide-view cameras 670 on the vehicle 600. Inaddition, long-range camera(s) 698 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 698 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 668 may also be included in a front-facingconfiguration. The stereo camera(s) 668 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 668 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 668 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 600 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 674 (e.g., four surround cameras 674 asillustrated in FIG. 6B) may be positioned to on the vehicle 600. Thesurround camera(s) 674 may include wide-view camera(s) 670, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 674 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 600 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 698,stereo camera(s) 668), infrared camera(s) 672, etc.), as describedherein.

FIG. 6C is a block diagram of an example system architecture for theexample autonomous vehicle 600 of FIG. 6A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 600 in FIG.6C are illustrated as being connected via bus 602. The bus 602 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 600 used to aid in control of various features and functionalityof the vehicle 600, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 602 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 602, this is notintended to be limiting. For example, there may be any number of busses602, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses602 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 602 may be used for collisionavoidance functionality and a second bus 602 may be used for actuationcontrol. In any example, each bus 602 may communicate with any of thecomponents of the vehicle 600, and two or more busses 602 maycommunicate with the same components. In some examples, each SoC 604,each controller 636, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle600), and may be connected to a common bus, such the CAN bus.

The vehicle 600 may include one or more controller(s) 636, such as thosedescribed herein with respect to FIG. 6A. The controller(s) 636 may beused for a variety of functions. The controller(s) 636 may be coupled toany of the various other components and systems of the vehicle 600, andmay be used for control of the vehicle 600, artificial intelligence ofthe vehicle 600, infotainment for the vehicle 600, and/or the like.

The vehicle 600 may include a system(s) on a chip (SoC) 604. The SoC 604may include CPU(s) 606, GPU(s) 608, processor(s) 610, cache(s) 612,accelerator(s) 614, data store(s) 616, and/or other components andfeatures not illustrated. The SoC(s) 604 may be used to control thevehicle 600 in a variety of platforms and systems. For example, theSoC(s) 604 may be combined in a system (e.g., the system of the vehicle600) with an HD map 622 which may obtain map refreshes and/or updatesvia a network interface 624 from one or more servers (e.g., server(s)678 of FIG. 6D).

The CPU(s) 606 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 606 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 606may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 606 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 606 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)606 to be active at any given time.

The CPU(s) 606 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 606may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 608 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 608 may be programmable and may beefficient for parallel workloads. The GPU(s) 608, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 608 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 608 may include at least eight streamingmicroprocessors. The GPU(s) 608 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 608 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 608 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 608 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 608 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 608 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 608 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 608 to access the CPU(s) 606 page tables directly. Insuch examples, when the GPU(s) 608 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 606. In response, the CPU(s) 606 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 608. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 606 and the GPU(s) 608, thereby simplifying the GPU(s) 608programming and porting of applications to the GPU(s) 608.

In addition, the GPU(s) 608 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 608 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 604 may include any number of cache(s) 612, including thosedescribed herein. For example, the cache(s) 612 may include an L3 cachethat is available to both the CPU(s) 606 and the GPU(s) 608 (e.g., thatis connected both the CPU(s) 606 and the GPU(s) 608). The cache(s) 612may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 604 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 600—such as processingDNNs. In addition, the SoC(s) 604 may include a floating point unit(s)(FPU(s))— or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 606 and/or GPU(s) 608.

The SoC(s) 604 may include one or more accelerators 614 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 604 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 608 and to off-load some of the tasks of theGPU(s) 608 (e.g., to free up more cycles of the GPU(s) 608 forperforming other tasks). As an example, the accelerator(s) 614 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 608, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 608 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 608 and/or other accelerator(s) 614.

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 606. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 614. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 604 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 614 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 666 output thatcorrelates with the vehicle 600 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 664 or RADAR sensor(s) 660), amongothers.

The SoC(s) 604 may include data store(s) 616 (e.g., memory). The datastore(s) 616 may be on-chip memory of the SoC(s) 604, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 616 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 612 may comprise L2 or L3 cache(s) 612. Reference to thedata store(s) 616 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 614, as described herein.

The SoC(s) 604 may include one or more processor(s) 610 (e.g., embeddedprocessors). The processor(s) 610 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 604 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 604 thermals and temperature sensors, and/ormanagement of the SoC(s) 604 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 604 may use thering-oscillators to detect temperatures of the CPU(s) 606, GPU(s) 608,and/or accelerator(s) 614. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 604 into a lower powerstate and/or put the vehicle 600 into a chauffeur to safe stop mode(e.g., bring the vehicle 600 to a safe stop).

The processor(s) 610 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 610 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 610 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 610 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 610 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 610 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)670, surround camera(s) 674, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 608 is not required tocontinuously render new surfaces. Even when the GPU(s) 608 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 608 to improve performance and responsiveness.

The SoC(s) 604 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 604 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 604 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 604 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 664, RADAR sensor(s) 660,etc. that may be connected over Ethernet), data from bus 602 (e.g.,speed of vehicle 600, steering wheel position, etc.), data from GNSSsensor(s) 658 (e.g., connected over Ethernet or CAN bus). The SoC(s) 604may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 606 from routine data management tasks.

The SoC(s) 604 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 604 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 614, when combined with the CPU(s) 606, the GPU(s) 608,and the data store(s) 616, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 620) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 608.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 600. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 604 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 696 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 604 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)658. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 662, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 618 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 604 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 618 may include an X86 processor,for example. The CPU(s) 618 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 604, and/or monitoring the statusand health of the controller(s) 636 and/or infotainment SoC 630, forexample.

The vehicle 600 may include a GPU(s) 620 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 604 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 620 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 600.

The vehicle 600 may further include the network interface 624 which mayinclude one or more wireless antennas 626 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 624 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 678 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 600information about vehicles in proximity to the vehicle 600 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 600).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 600.

The network interface 624 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 636 tocommunicate over wireless networks. The network interface 624 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 600 may further include data store(s) 628 which may includeoff-chip (e.g., off the SoC(s) 604) storage. The data store(s) 628 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 600 may further include GNSS sensor(s) 658. The GNSSsensor(s) 658 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS)sensors, etc.), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)658 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 600 may further include RADAR sensor(s) 660. The RADARsensor(s) 660 may be used by the vehicle 600 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 660 may usethe CAN and/or the bus 602 (e.g., to transmit data generated by theRADAR sensor(s) 660) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 660 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 660 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 660may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 600 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 600 lane.

Mid-range RADAR systems may include, as an example, a range of up to 660m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 650 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 600 may further include ultrasonic sensor(s) 662. Theultrasonic sensor(s) 662, which may be positioned at the front, back,and/or the sides of the vehicle 600, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 662 may be used, and different ultrasonic sensor(s) 662 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 662 may operate at functional safety levels of ASILB.

The vehicle 600 may include LIDAR sensor(s) 664. The LIDAR sensor(s) 664may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 664 maybe functional safety level ASIL B. In some examples, the vehicle 600 mayinclude multiple LIDAR sensors 664 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 664 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 664 may have an advertised rangeof approximately 600 m, with an accuracy of 2 cm-3 cm, and with supportfor a 600 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 664 may be used. In such examples,the LIDAR sensor(s) 664 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 600.The LIDAR sensor(s) 664, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)664 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 600. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)664 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 666. The IMU sensor(s) 666may be located at a center of the rear axle of the vehicle 600, in someexamples. The IMU sensor(s) 666 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 666 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 666 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 666 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 666 may enable the vehicle 600to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 666. In some examples, the IMU sensor(s) 666 and theGNSS sensor(s) 658 may be combined in a single integrated unit.

The vehicle may include microphone(s) 696 placed in and/or around thevehicle 600. The microphone(s) 696 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 668, wide-view camera(s) 670, infrared camera(s) 672,surround camera(s) 674, long-range and/or mid-range camera(s) 698,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 600. The types of cameras useddepends on the embodiments and requirements for the vehicle 600, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 600. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 6A and FIG. 6B.

The vehicle 600 may further include vibration sensor(s) 642. Thevibration sensor(s) 642 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 642 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 600 may include an ADAS system 638. The ADAS system 638 mayinclude a SoC, in some examples. The ADAS system 638 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 660, LIDAR sensor(s) 664, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 600 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 600 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 624 and/or the wireless antenna(s) 626 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 600), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 600, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 660, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle600 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 600 if the vehicle 600 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 600 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 660, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 600, the vehicle 600itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 636 or a second controller 636). For example, in someembodiments, the ADAS system 638 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 638may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 604.

In other examples, ADAS system 638 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 638 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 638indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 600 may further include the infotainment SoC 630 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 630 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 600. For example, the infotainment SoC 630 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 634, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 630 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 638,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 630 may include GPU functionality. The infotainmentSoC 630 may communicate over the bus 602 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 600. Insome examples, the infotainment SoC 630 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 636(e.g., the primary and/or backup computers of the vehicle 600) fail. Insuch an example, the infotainment SoC 630 may put the vehicle 600 into achauffeur to safe stop mode, as described herein.

The vehicle 600 may further include an instrument cluster 632 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 632 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 632 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 630 and theinstrument cluster 632. In other words, the instrument cluster 632 maybe included as part of the infotainment SoC 630, or vice versa.

FIG. 6D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 600 of FIG. 6A, inaccordance with some embodiments of the present disclosure. The system676 may include server(s) 678, network(s) 690, and vehicles, includingthe vehicle 600. The server(s) 678 may include a plurality of GPUs684(A)-684(H) (collectively referred to herein as GPUs 684), PCIeswitches 682(A)-682(H) (collectively referred to herein as PCIe switches682), and/or CPUs 680(A)-680(B) (collectively referred to herein as CPUs680). The GPUs 684, the CPUs 680, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 688 developed by NVIDIA and/orPCIe connections 686. In some examples, the GPUs 684 are connected viaNVLink and/or NVSwitch SoC and the GPUs 684 and the PCIe switches 682are connected via PCIe interconnects. Although eight GPUs 684, two CPUs680, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 678 mayinclude any number of GPUs 684, CPUs 680, and/or PCIe switches. Forexample, the server(s) 678 may each include eight, sixteen, thirty-two,and/or more GPUs 684.

The server(s) 678 may receive, over the network(s) 690 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 678 may transmit, over the network(s) 690 and to the vehicles,neural networks 692, updated neural networks 692, and/or map information694, including information regarding traffic and road conditions. Theupdates to the map information 694 may include updates for the HD map622, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 692, the updated neural networks 692, and/or the mapinformation 694 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 678 and/or other servers).

The server(s) 678 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 690, and/or the machine learningmodels may be used by the server(s) 678 to remotely monitor thevehicles.

In some examples, the server(s) 678 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 678 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 684, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 678 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 678 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 600. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 600, suchas a sequence of images and/or objects that the vehicle 600 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 600 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 600 is malfunctioning, the server(s) 678 may transmit asignal to the vehicle 600 instructing a fail-safe computer of thevehicle 600 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 678 may include the GPU(s) 684 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 7 is a block diagram of an example computing device(s) 700 suitablefor use in implementing some embodiments of the present disclosure.Computing device 700 may include an interconnect system 702 thatdirectly or indirectly couples the following devices: memory 704, one ormore central processing units (CPUs) 706, one or more graphicsprocessing units (GPUs) 708, a communication interface 710, input/output(I/O) ports 712, input/output components 714, a power supply 716, one ormore presentation components 718 (e.g., display(s)), and one or morelogic units 720. In at least one embodiment, the computing device(s) 700may comprise one or more virtual machines (VMs), and/or any of thecomponents thereof may comprise virtual components (e.g., virtualhardware components). For non-limiting examples, one or more of the GPUs708 may comprise one or more vGPUs, one or more of the CPUs 706 maycomprise one or more vCPUs, and/or one or more of the logic units 720may comprise one or more virtual logic units. As such, a computingdevice(s) 700 may include discrete components (e.g., a full GPUdedicated to the computing device 700), virtual components (e.g., aportion of a GPU dedicated to the computing device 700), or acombination thereof.

Although the various blocks of FIG. 7 are shown as connected via theinterconnect system 702 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 718, such as a display device, may be consideredan I/O component 714 (e.g., if the display is a touch screen). Asanother example, the CPUs 706 and/or GPUs 708 may include memory (e.g.,the memory 704 may be representative of a storage device in addition tothe memory of the GPUs 708, the CPUs 706, and/or other components). Inother words, the computing device of FIG. 7 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.7 .

The interconnect system 702 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 702 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 706 may be directly connectedto the memory 704. Further, the CPU 706 may be directly connected to theGPU 708. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 702 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 700.

The memory 704 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 700. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 704 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device700. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 706 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 700 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 706 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 706 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 700 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 700, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 700 mayinclude one or more CPUs 706 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 706, the GPU(s) 708 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device700 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 708 may be an integrated GPU (e.g.,with one or more of the CPU(s) 706 and/or one or more of the GPU(s) 708may be a discrete GPU. In embodiments, one or more of the GPU(s) 708 maybe a coprocessor of one or more of the CPU(s) 706. The GPU(s) 708 may beused by the computing device 700 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 708 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 708may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 708may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 706 received via ahost interface). The GPU(s) 708 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory704. The GPU(s) 708 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 708 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 706 and/or the GPU(s)708, the logic unit(s) 720 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 700 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 706, the GPU(s)708, and/or the logic unit(s) 720 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 720 may be part of and/or integrated in one ormore of the CPU(s) 706 and/or the GPU(s) 708 and/or one or more of thelogic units 720 may be discrete components or otherwise external to theCPU(s) 706 and/or the GPU(s) 708. In embodiments, one or more of thelogic units 720 may be a coprocessor of one or more of the CPU(s) 706and/or one or more of the GPU(s) 708.

Examples of the logic unit(s) 720 include one or more processing coresand/or components thereof, such as Tensor Cores (TCs), Tensor ProcessingUnits(TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs),Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs),Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), ArtificialIntelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs),Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits(ASICs), Floating Point Units (FPUs), input/output (I/O) elements,peripheral component interconnect (PCI) or peripheral componentinterconnect express (PCIe) elements, and/or the like.

The communication interface 710 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 700to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 710 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet.

The I/O ports 712 may enable the computing device 700 to be logicallycoupled to other devices including the I/O components 714, thepresentation component(s) 718, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 700.Illustrative I/O components 714 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 714 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 700. Thecomputing device 700 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 700 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 700 to render immersive augmented reality or virtual reality.

The power supply 716 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 716 may providepower to the computing device 700 to enable the components of thecomputing device 700 to operate.

The presentation component(s) 718 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 718 may receivedata from other components (e.g., the GPU(s) 708, the CPU(s) 706, etc.),and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG. 8 illustrates an example data center 800 that may be used in atleast one embodiments of the present disclosure. The data center 800 mayinclude a data center infrastructure layer 810, a framework layer 820, asoftware layer 830, and/or an application layer 840.

As shown in FIG. 8 , the data center infrastructure layer 810 mayinclude a resource orchestrator 812, grouped computing resources 814,and node computing resources (“node C.R.s”) 816(1)-816(N), where “N”represents any whole, positive integer. In at least one embodiment, nodeC.R.s 816(1)-816(N) may include, but are not limited to, any number ofcentral processing units (“CPUs”) or other processors (includingaccelerators, field programmable gate arrays (FPGAs), graphicsprocessors or graphics processing units (GPUs), etc.), memory devices(e.g., dynamic read-only memory), storage devices (e.g., solid state ordisk drives), network input/output (“NW I/O”) devices, network switches,virtual machines (“VMs”), power modules, and/or cooling modules, etc. Insome embodiments, one or more node C.R.s from among node C.R.s816(1)-816(N) may correspond to a server having one or more of theabove-mentioned computing resources. In addition, in some embodiments,the node C.R.s 816(1)-8161(N) may include one or more virtualcomponents, such as vGPUs, vCPUs, and/or the like, and/or one or more ofthe node C.R.s 816(1)-816(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 814 may includeseparate groupings of node C.R.s 816 housed within one or more racks(not shown), or many racks housed in data centers at variousgeographical locations (also not shown). Separate groupings of nodeC.R.s 816 within grouped computing resources 814 may include groupedcompute, network, memory or storage resources that may be configured orallocated to support one or more workloads. In at least one embodiment,several node C.R.s 816 including CPUs, GPUs, and/or other processors maybe grouped within one or more racks to provide compute resources tosupport one or more workloads. The one or more racks may also includeany number of power modules, cooling modules, and/or network switches,in any combination.

The resource orchestrator 822 may configure or otherwise control one ormore node C.R.s 816(1)-816(N) and/or grouped computing resources 814. Inat least one embodiment, resource orchestrator 822 may include asoftware design infrastructure (“SDI”) management entity for the datacenter 800. The resource orchestrator 822 may include hardware,software, or some combination thereof.

In at least one embodiment, as shown in FIG. 8 , framework layer 820 mayinclude a job scheduler 832, a configuration manager 834, a resourcemanager 836, and/or a distributed file system 838. The framework layer820 may include a framework to support software 832 of software layer830 and/or one or more application(s) 842 of application layer 840. Thesoftware 832 or application(s) 842 may respectively include web-basedservice software or applications, such as those provided by Amazon WebServices, Google Cloud and Microsoft Azure. The framework layer 820 maybe, but is not limited to, a type of free and open-source software webapplication framework such as Apache Spark™ (hereinafter “Spark”) thatmay utilize distributed file system 838 for large-scale data processing(e.g., “big data”). In at least one embodiment, job scheduler 832 mayinclude a Spark driver to facilitate scheduling of workloads supportedby various layers of data center 800. The configuration manager 834 maybe capable of configuring different layers such as software layer 830and framework layer 820 including Spark and distributed file system 838for supporting large-scale data processing. The resource manager 836 maybe capable of managing clustered or grouped computing resources mappedto or allocated for support of distributed file system 838 and jobscheduler 832. In at least one embodiment, clustered or groupedcomputing resources may include grouped computing resource 814 at datacenter infrastructure layer 810. The resource manager 1036 maycoordinate with resource orchestrator 812 to manage these mapped orallocated computing resources.

In at least one embodiment, software 832 included in software layer 830may include software used by at least portions of node C.R.s816(1)-816(N), grouped computing resources 814, and/or distributed filesystem 838 of framework layer 820. One or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 842 included in applicationlayer 840 may include one or more types of applications used by at leastportions of node C.R.s 816(1)-816(N), grouped computing resources 814,and/or distributed file system 838 of framework layer 820. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.),and/or other machine learning applications used in conjunction with oneor more embodiments.

In at least one embodiment, any of configuration manager 834, resourcemanager 836, and resource orchestrator 812 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. Self-modifying actions mayrelieve a data center operator of data center 800 from making possiblybad configuration decisions and possibly avoiding underutilized and/orpoor performing portions of a data center.

The data center 800 may include tools, services, software or otherresources to train one or more machine learning models or predict orinfer information using one or more machine learning models according toone or more embodiments described herein. For example, a machinelearning model(s) may be trained by calculating weight parametersaccording to a neural network architecture using software and/orcomputing resources described above with respect to the data center 800.In at least one embodiment, trained or deployed machine learning modelscorresponding to one or more neural networks may be used to infer orpredict information using resources described above with respect to thedata center 800 by using weight parameters calculated through one ormore training techniques, such as but not limited to those describedherein.

In at least one embodiment, the data center 800 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, and/orother hardware (or virtual compute resources corresponding thereto) toperform training and/or inferencing using above-described resources.Moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of thecomputing device(s) 700 of FIG. 7 —e.g., each device may include similarcomponents, features, and/or functionality of the computing device(s)700. In addition, where backend devices (e.g., servers, NAS, etc.) areimplemented, the backend devices may be included as part of a datacenter 800, an example of which is described in more detail herein withrespect to FIG. 8 .

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example computing device(s) 700described herein with respect to FIG. 7 . By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: determining, based at leaston sensor data generated using one or more sensors of a machine, a setof one or more dynamic objects that are located in a drivable region ofan environment; determining one or more classes associated with the setof dynamic objects; determining, based at least on the one or moreclasses, a subset of dynamic objects from the set of dynamic objects;and performing object tracking with respect to the subset of dynamicobjects.
 2. The method of claim 1, wherein: the one or more classesinclude at least a vehicle class associated with a dynamic object of theset of dynamic objects; and the determining the subset of dynamicobjects comprises determining, based at least on the vehicle class, toinclude the dynamic object in the subset of dynamic objects.
 3. Themethod of claim 1, wherein: the one or more classes include at least anon-vehicle class associated with a dynamic object of the set of dynamicobjects; and the determining the subset of dynamic objects comprisesdetermining, based at least on the non-vehicle class, not to include thedynamic object in the subset of dynamic objects.
 4. The method of claim1, wherein the determining the set of dynamic objects that are locatedin the drivable region of the environment comprises determining, basedat least on the sensor data, the set of dynamic objects that are locatedwithin one or more lanes of the environment.
 5. The method of claim 1,wherein the determining the set of dynamic objects that are located inthe drivable region of the environment comprises: determining a firstlane in which the machine is navigating; determining at least one secondlane that is adjacent to the first lane; and determining, based at leaston the sensor data, the set of dynamic objects are located within atleast one of: the first lane or the at least one second lane.
 6. Themethod of claim 1, further comprising: determining a path associatedwith the machine within the environment, wherein the determining the setof dynamic objects is further based at least on the path associated withthe machine.
 7. The method of claim 1, further comprising: determiningone or more scores associated with the one or more classes, wherein thedetermining the subset of dynamic objects is further based at least onthe one or more scores.
 8. The method of claim 1, wherein thedetermining the set of dynamic objects that are located in the drivableregion of the environment comprises: determining, based at least on thesensor data, objects that are located within the environment;determining at least one first object of the objects that is static orat least one second object of the objects that is located within anon-drivable region of the environment; and determining the set ofdynamic objects based at least on filtering out at least one of: the atleast one first object or the at least one second object.
 9. A systemcomprising: one or more processing units to: determine, based at leaston sensor data generated using one or more sensors of an ego-machine, aset of objects that are located within an environment; determine, basedat least on one or more classes associated with the set of objects andlocations of the set of objects with respect to a drivable region of theenvironment, a subset of objects from the set of objects; and performone or more object tracking operations with respect to the subset ofobjects.
 10. The system of claim 9, wherein the determination of thesubset of objects comprises: determining that the one or more classesincludes a vehicle class associated with an object from the set ofobjects; and determining, based at least on the vehicle class, toinclude the object in the subset of objects.
 11. The system of claim 9,wherein the determination of the subset of objects comprises:determining that the one or more classes includes a non-vehicle classassociated with an object from the set of objects; and determining,based at least on the non-vehicle class, to not include the object inthe subset of objects.
 12. The system of claim 9, wherein thedetermination of the subset of objects comprises: determining one ormore lanes within the environment; and determining that objects includedin the subset of objects are located within the one or more lanes. 13.The system of claim 9, wherein the determination of the subset ofobjects comprises: determining a first lane for which the ego-machine isnavigating; determining at least one second lane that is adjacent to thefirst lane; and determining that objects in the subset of objects arelocated within at least one of: the first lane or the at least onesecond lane.
 14. The system of claim 9, wherein the one or moreprocessing units are further to: determine a path associated with theego-machine within the environment, wherein the determination of thesubset of objects is further based at least on the path associated withthe ego-machine.
 15. The system of claim 9, wherein the one or moreprocessing units are further to: determine one or more scores indicatingwhether objects in the set of objects are located within the drivableregion, wherein the determination of the subset of objects is furtherbased at least on the one or more scores.
 16. The system of claim 9,wherein the set of objects includes one or more dynamic objects locatedwithin the environment.
 17. The system of claim 9, wherein the system iscomprised in at least one of: a control system for an autonomous orsemi-autonomous machine; a perception system for an autonomous orsemi-autonomous machine; a system for performing simulation operations;a system for performing digital twin operations; a system for performingdeep learning operations; a system implemented using an edge device; asystem implemented using a robot; a system incorporating one or morevirtual machines (VMs); a system implemented at least partially in adata center; or a system implemented at least partially using cloudcomputing resources.
 18. A processor comprising: one or more processingunits to perform one or more object tracking operations with respect toa subset of detected objects within an environment, wherein the subsetof detected objects is determined based at least on filtering out one ormore objects from a set of detected objects based at least on one ormore locations of the one or more objects within one or more lanes of anenvironment and one or more object classes associated with the one ormore objects.
 19. The processor of claim 18, wherein the subset ofdetected objects is further determined based at least on a pathassociated with an ego-machine within the environment.
 20. The processorof claim 18, wherein the processor is comprised in at least one of: acontrol system for an autonomous or semi-autonomous machine; aperception system for an autonomous or semi-autonomous machine; a systemfor performing simulation operations; a system for performing digitaltwin operations; a system for performing deep learning operations; asystem implemented using an edge device; a system implemented using arobot; a system incorporating one or more virtual machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.